Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Sex Classification of Face Areas
1998
Human subjects and an artificial neural network, composed of an autoassociative memory and a perceptron, gender classified the same 160 frontal face images (80 male and 80 female). All 160 face images were presented under three conditions (1) full face image with the hair cropped (2) top portion only of the Condition 1 image (3) bottom portion only of the Condition 1 image. Predictions from simulations using Condition 1 stimuli for training and testing novel stimuli in Conditions 1, 2, and 3, were compared to human subject performance. Although the network showed a fair ability to generalize learning to new stimuli under the three conditions, performing from 66 to 78% correctly on novel fa…
Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion
2020
Recognition systems using multimodal biometrics attracts attention because they improve recognition efficiency and high-security level compared to the unimodal biometrics system. In this study, the authors present a secure multimodal biometrics recognition system based on the deep learning method that uses convolutional neural networks (CNNs). The authors propose two multimodal architectures using the finger knuckle print (FKP) and the finger vein (FV) biometrics with different levels of fusion: the features level fusion and scores level fusion. The features extraction for FKP and FV are performed using transfer learning CNN architectures: AlexNet, VGG16, and ResNet50. The key step aims to …
Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
2020
In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery. peerReviewed
An immune system model in discrete time based on the analogy with the central nervous system
1988
Jerne's model for the immune system formulated in terms of a neural network recently proposed by Weisbuch and Atlan is generalized to interactions with continuous coupling coefficients. It is shown that even the extended model can be solved analytically without the aid of computer simulations and exhibits one additional attractor, which corresponds to a configuration with high concentrations of active killer cells eventually causing death of the organism.
Predicting the Success of Blastocyst Implantation from Morphokinetic Parameters Estimated through CNNs and Sum of Absolute Differences
2019
The process of In Vitro Fertilization deals nowadays with the challenge of selecting viable embryos with the highest probability of success in the implantation. In this topic, we present a computer-vision-based system to analyze the videos related to days of embryo development which automatically extracts morphokinetic features and estimates the success of implantation. A robust algorithm to detect the embryo in the culture image is proposed to avoid artifacts. Then, the ability of Convolutional Neural Networks (CNNs) for predicting the number of cells per frame is novelty combined with the Sum of Absolute Differences (SAD) signal in charge of capturing the amount of intensity changes durin…
Evolving Tree Algorithm Modifications
2007
There are many variants of the original self-organizing neural map algorithm proposed by Kohonen. One of the most recent is the Evolving Tree, a tree-shaped self-organizing network which has many interesting characteristics. This network builds a tree structure splitting the input dataset during learning. This paper presents a speed-up modification of the original training algorithm useful when the Evolving Tree network is used with complex data as images or video. After a measurement of the effectiveness an application of the modified algorithm in image segmentation is presented.
Neural network prediction of the AE index from the PC index
1999
Abstract It is shown that although the power spectra of the AE and PC data are quite similar they show differencies in structure function analysis. While the AE time series has a clear drop in the slope of the structure function (SF) after the first 2 hours, the slope of the SF of the PC data decreases gradually and at a little longer time scale. It is also shown by using 15-min averaged data, that both SFs are periodic with a clear diurnal variation. The PC time series seems to have a more pronounced periodicity, probably because it is measured at a single station at Thule. The AE index has been derived from the PC index for 7.5 minutes ahead by different methods. All these predictions gav…
Enhancing Speed Loop PI Controllers with Adaptive Feed-forward Neural Networks: Application to Induction Motor Drives
2022
This paper proposes the idea to improve the performance of the speed loop PI controller by using feed-forward and adaptive control actions. Indeed, when the system to be controlled is required to track a rapidly changing reference frame, higher bandwidth is usually required, making the system more sensitive to noise and consequently less robust. In such cases, to achieve a better performance in reference tracking while keeping noise rejection capacity, one idea is to use a feed-forward controller, employed to enhance the required tracking, leaving the feedback action to stabilize the system and suppress higher frequency disturbance. As such, this paper analysis the classical PI based field …
An approach based on the Adaptive Resonance Theory for analysing the viability of recommender systems in a citizen Web portal
2007
This paper proposes a methodology to optimise the future accuracy of a collaborative recommender application in a citizen Web portal. There are four stages namely, user modelling, benchmarking of clustering algorithms, prediction analysis and recommendation. The first stage is to develop analytical models of common characteristics of Web-user data. These artificial data sets are then used to evaluate the performance of clustering algorithms, in particular benchmarking the ART2 neural network with K-means clustering. Afterwards, it is evaluated the predictive accuracy of the clusters applied to a real-world data set derived from access logs to the citizen Web portal Infoville XXI (http://www…
Bidirected Information Flow in the High-Level Visual Cortex
2021
Understanding the brain function requires investigating information transfer across brain regions. Shannon began the remarkable new field of information theory in 1948. It basically can be divided into two categories: directed and undirected information-theoretical approaches. As we all know, neural signals are typically nonlinear and directed flow between brain regions. We can use directed information to quantify feed-forward information flow, feedback information, and instantaneous influence in the high-level visual cortex. Moreover, neural signals have bidirectional information flow properties and are not captured by the transfer entropy approach. Therefore, we used directed information …